Intelligent air cleaner, indoor air quality control method and control apparatus using intelligent air cleaner

ABSTRACT

An indoor air quality control method using an intelligent air cleaner is disclosed. An indoor air quality control method using an intelligent air cleaner according to an embodiment of the present invention can predict indoor dust concentration progress on the basis of output values of a learning model having received dust concentration data as input values when the dust concentration data of an indoor place where the air cleaner is located is received from the air cleaner, and determine whether ventilation is required by comparing the predicted progress with outside dust concentration data received from the Meteorological Administration server. Accordingly, indoor dust concentration changes can be predicted and an appropriate ventilation time can be recommended. The intelligent air cleaner and the indoor air quality control method using the same of the present invention can be associated with artificial intelligence modules, devices related with the 5G service, and the like.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is the National Phase of PCT International ApplicationNo. PCT/KR2019/008679, filed on Jul. 12, 2019, which is hereby expresslyincorporated by reference into the present application.

Technical Field

The present invention relates to an intelligent air cleaner, an indoorair quality control method and control apparatus using the intelligentair cleaner, and more specifically, to an intelligent air cleanercapable of recommending an appropriate ventilation time by predictingindoor dust concentration progress, and an indoor air quality controlmethod and control apparatus using the intelligent air cleaner.

Background Art

An air cleaner has a function of eliminating fine dusts or harmfulsubstances in the air and purifying the air. Such an air cleaner isrequired to minimize energy consumption and effectively controlpollutants in the air.

Meanwhile, in cases in which a degree of pollution is very high, such ascooking and cleaning for a long time in indoor spaces, ventilation maybe a more efficient method rather than operating an air cleaner indoors.

Furthermore, considering that the lifespan of a filter may beconsiderably affected when an air cleaner is continuously driven inorder to eliminate indoor fine dusts, it is necessary to activelyreflect a state of outside air quality in air purification.

DISCLOSURE Technical Problem

An object of the present invention is to solve the aforementionednecessity and/or problems.

Further, the present invention provides an indoor air quality controlmethod using an intelligent air cleaner which can recommend anappropriate ventilation time by predicting indoor dust concentrationprogress.

Further, the present invention provides an indoor air quality controlmethod using an intelligent air cleaner which can recommend anappropriate ventilation time by comparing a predicted indoor dustconcentration with a degree of outside air pollution.

Further, the present invention provides an indoor air quality controlmethod using an intelligent air cleaner which can minimize the amount ofinput data using a deep learning model and efficiently manage thelifespan of a filter of the air cleaner by predicting indoor dustconcentration progress on the basis of the minimized amount of inputdata and recommending ventilation instead of air purification using theair cleaner.

Further, the present invention provides an indoor air quality controlmethod using an intelligent air cleaner which can efficiently manage theair cleaner by comparing indoor dust concentration progress with areal-time outside fine dust concentration and providing a ventilationalarm to the air cleaner or a user terminal.

Technical Solution

An indoor air quality control method using an intelligent air cleaneraccording to one aspect of the present invention includes: receiving,from the air cleaner, dust concentration data of an indoor place wherethe air cleaner is located; predicting indoor dust concentrationprogress on the basis of output values of a learning model having thereceived dust concentration data as input values; receiving outside dustconcentration data from an external server; determining whetherventilation is required by comparing the predicted indoor dustconcentration progress with the outside dust concentration data; andcontrolling an alarm to be output to the air cleaner or a mobileterminal associated with the air cleaner according to whetherventilation is required, wherein the received dust concentration data isdata exceeding a predetermined reference value among data sensed atpredetermined intervals.

The receiving of the indoor dust concentration data from the air cleanermay include receiving a certain percentage or higher of data in a badstate on the basis of PM 2.5 forecast from among data sensed by the aircleaner at predetermined intervals.

The predicting of the indoor dust concentration progress may include:determining whether dust concentration data continuously sensed N timesexceeds the predetermined reference value; and defining data of N dustconcentrations as input values of a deep learning model and predictingthe indoor dust concentration progress through output values of the deeplearning model.

The indoor air quality control method may further include, when progressof additional N dust concentrations sensed after the N dustconcentrations are sensed is predicted to increase as a result ofprediction of the indoor dust concentration progress: requesting theoutside dust concentration data from the Meteorological Administrationserver; and determining that ventilation is required when an averagevalue of input data of the deep learning model is greater than theoutside dust concentration data received from the MeteorologicalAdministration server.

The indoor dust concentration progress may be predicted as one of apattern in which the same number of dust concentrations as the number ofpieces of dust concentration data received from the intelligent aircleaner increase, a pattern in which the dust concentrations decreaseand a pattern in which the dust concentrations remains in a currentstate.

The determining of whether ventilation is required may includedetermining that ventilation is required when the indoor dustconcentration progress is determined to be the increasing pattern or thecurrent state remaining pattern.

The determining of whether ventilation is required may include:determining that ventilation is not required when the indoor dustconcentration progress is predicted as the increasing pattern and anoutside dust concentration received from the external server is higherthan the indoor dust concentration; and controlling the air cleaner tocontinuously operate.

The determining of whether ventilation is required may further includepredicting a ventilation time, wherein it is determined that ventilationis required when the indoor dust concentration progress is predicted asthe decreasing pattern and it is predicted that the outside dustconcentration is lower than the indoor dust concentration at a specifictime of the decreasing pattern.

The controlling of output of the alarm includes controlling an indoordust concentration state and whether ventilation is required to beoutput through audio.

The receiving of the indoor dust concentration data may further includereceiving, in a state in which the air cleaner is powered off, theindoor dust concentration data sensed through a sensing unit in a statein which the air cleaner is in a standby state, and the indoor airquality control method may further include controlling the aircontroller to be activated when it is determined that ventilation isrequired.

The determining of whether ventilation is required may includeadaptively controlling a ventilation recommendation standard inconsideration of characteristics of an occupant residing in the indoorplace.

An indoor air quality control apparatus using an intelligent air cleaneraccording to another aspect of the present invention includes: an RFcommunication unit a storage unit storing a deep learning model; and aprocessor configured to determine whether to perform ventilation in aspace in which the air cleaner is located on the basis of indoor dustconcentration data received from the air cleaner and outside dustconcentration data received from the Meteorological Administrationserver, wherein the received dust concentration data is data exceeding apredetermined reference value among data sensed at predeterminedintervals, and the processor predicts indoor dust concentration progresson the basis of output values of the deep learning model having thereceived dust concentration data as input values and controls an alarmto be output to the air cleaner or a mobile terminal associated with theair cleaner according to whether ventilation is required.

An indoor air quality control system according to another aspect of thepresent invention includes: an intelligence air cleaner for acquiringindoor dust concentration data; and a cloud server for receiving theindoor dust concentration data from the air cleaner, wherein thereceived dust concentration data is data exceeding a predeterminedreference value among data sensed by the air cleaner at predeterminedintervals, and the cloud server predicts indoor dust concentrationprogress on the basis of output values of a deep learning model havingthe received dust concentration data as input values, receives outsidedust concentration data from an external server, determines whetherventilation is required by comparing the predicted indoor dustconcentration progress with the outside dust concentration data, andoutputs an alarm to the air cleaner or a mobile terminal associated withthe air cleaner according to whether ventilation is required.

A non-transitory computer-readable medium according to another aspect ofthe present invention stores a computer-executable component configuredto be executed in one or more processors of a computer device, whereinthe computer-executable component is configured: to receive, from an aircleaner, dust concentration data of an indoor place where the aircleaner is located; to predict indoor dust concentration progress on thebasis of output values of a learning model having the received dustconcentration data as input values; to receive outside dustconcentration data from an external server; to determine whetherventilation is required by comparing the predicted indoor dustconcentration progress with the outside dust concentration data; and tocontrol an alarm to be output to the air cleaner or a mobile terminalassociated with the air cleaner according to whether ventilation isrequired, wherein the received dust concentration data includes dataexceeding a predetermined reference value among data sensed atpredetermined intervals.

Advantageous Effects

The effects of the indoor air quality control method using anintelligent air cleaner according to an embodiment of the presentinvention will be described below.

According to the present invention, it is possible to recommend anappropriate ventilation time by predicting indoor dust concentrationprogress.

Further, according to the present invention, it is possible to recommendan appropriate ventilation time by comparing a predicted indoor dustconcentration with a degree of outside air pollution.

Further, according to the present invention, it is possible to minimizethe amount of input data using a deep learning model and efficientlymanage the lifespan of a filter of the air cleaner by predicting indoordust concentration progress on the basis of the minimized amount ofinput data and recommending ventilation instead of air purificationusing the air cleaner.

Further, according to the present invention, it is possible toefficiently manage the air cleaner by comparing indoor dustconcentration progress with a real-time outside fine dust concentrationand providing a ventilation alarm to the air cleaner or a user terminal.

It will be appreciated by persons skilled in the art that the effectsthat could be achieved with the present invention are not limited towhat has been particularly described hereinabove and the above and othereffects that the present invention could achieve will be more clearlyunderstood from the following detailed description.

DESCRIPTION OF DRAWINGS

Accompanying drawings included as a part of the detailed description forhelping understand the present invention provide embodiments of thepresent invention and are provided to describe technical features of thepresent invention with the detailed description.

FIG. 1 is a block diagram of a wireless communication system to whichmethods proposed in the disclosure are applicable.

FIG. 2 shows an example of a signal transmission/reception method in awireless communication system.

FIG. 3 shows an example of basic operations of a user equipment and a 5Gnetwork in a 5G communication system.

FIG. 4 schematically illustrates a system for implementing an indoor airquality control method using an intelligent air cleaner according to anembodiment of the present invention.

FIG. 5 is a block diagram of an AI apparatus applicable to embodimentsof the present invention.

FIG. 6 is an exemplary block diagram of an indoor air quality controlapparatus using an intelligent air cleaner according to an embodiment ofthe present invention.

FIG. 7 illustrates a data flow for implementing the indoor air qualitycontrol method using an intelligent air cleaner according to anembodiment of the present invention.

FIG. 8 is a flowchart of the indoor air quality control method using anintelligent air cleaner according to an embodiment of the presentinvention.

FIG. 9 is a diagram for describing a detailed operation of a cloudserver for performing the indoor air quality control method using anintelligent air cleaner according to an embodiment of the presentinvention.

FIG. 10 is a diagram for describing deep learning model learningstandards according to an embodiment of the present invention.

FIG. 11 is a diagram for describing various examples of indoor airquality patterns used for deep learning model learning through dataclustering according to an embodiment of the present invention.

FIG. 12 is a diagram for describing deep learning model learningpatterns through input factors and result factors according to anembodiment of the present invention.

FIG. 13 is a diagram for describing an example of determining necessityfor ventilation using a learned deep learning model according to anembodiment of the present invention.

FIGS. 14 and 15 are diagrams for describing results of tests to whichthe indoor air quality control method using an intelligent air cleaneraccording to the present invention is applied.

The accompanying drawings, which are included to provide a furtherunderstanding of the invention and are incorporated in and constitute apart of this specification, illustrate embodiments of the invention andtogether with the description serve to explain the principles of theinvention.

MODE FOR INVENTION

Hereinafter, embodiments of the disclosure will be described in detailwith reference to the attached drawings. The same or similar componentsare given the same reference numbers and redundant description thereofis omitted. The suffixes “module” and “unit” of elements herein are usedfor convenience of description and thus can be used interchangeably anddo not have any distinguishable meanings or functions. Further, in thefollowing description, if a detailed description of known techniquesassociated with the present invention would unnecessarily obscure thegist of the present invention, detailed description thereof will beomitted. In addition, the attached drawings are provided for easyunderstanding of embodiments of the disclosure and do not limittechnical spirits of the disclosure, and the embodiments should beconstrued as including all modifications, equivalents, and alternativesfalling within the spirit and scope of the embodiments.

While terms, such as “first”, “second”, etc., may be used to describevarious components, such components must not be limited by the aboveterms. The above terms are used only to distinguish one component fromanother.

When an element is “coupled” or “connected” to another element, itshould be understood that a third element may be present between the twoelements although the element may be directly coupled or connected tothe other element. When an element is “directly coupled” or “directlyconnected” to another element, it should be understood that no elementis present between the two elements.

The singular forms are intended to include the plural forms as well,unless the context clearly indicates otherwise.

In addition, in the specification, it will be further understood thatthe terms “comprise” and “include” specify the presence of statedfeatures, integers, steps, operations, elements, components, and/orcombinations thereof, but do not preclude the presence or addition ofone or more other features, integers, steps, operations, elements,components, and/or combinations.

Hereinafter, 5G communication (5th generation mobile communication)required by an apparatus requiring AI processed information and/or an AIprocessor will be described through paragraphs A through G.

A. Example of Block Diagram of UE and 5G Network

FIG. 1 is a block diagram of a wireless communication system to whichmethods proposed in the disclosure are applicable.

Referring to FIG. 1 , a device (autonomous device) including anautonomous module is defined as a first communication device (910 ofFIG. 1 ), and a processor 911 can perform detailed autonomousoperations.

A 5G network including another vehicle communicating with the autonomousdevice is defined as a second communication device (920 of FIG. 1 ), anda processor 921 can perform detailed autonomous operations.

The 5G network may be represented as the first communication device andthe autonomous device may be represented as the second communicationdevice.

For example, the first communication device or the second communicationdevice may be a base station, a network node, a transmission terminal, areception terminal, a wireless device, a wireless communication device,an autonomous device, or the like.

For example, the first communication device or the second communicationdevice may be a base station, a network node, a transmission terminal, areception terminal, a wireless device, a wireless communication device,a vehicle, a vehicle having an autonomous function, a connected car, adrone (Unmanned Aerial Vehicle, UAV), and AI (Artificial Intelligence)module, a robot, an AR (Augmented Reality) device, a VR (VirtualReality) device, an MR (Mixed Reality) device, a hologram device, apublic safety device, an MTC device, an IoT device, a medical device, aFin Tech device (or financial device), a security device, aclimate/environment device, a device associated with 5G services, orother devices associated with the fourth industrial revolution field.

For example, a terminal or user equipment (UE) may include a cellularphone, a smart phone, a laptop computer, a digital broadcast terminal,personal digital assistants (PDAs), a portable multimedia player (PMP),a navigation device, a slate PC, a tablet PC, an ultrabook, a wearabledevice (e.g., a smartwatch, a smart glass and a head mounted display(HMD)), etc. For example, the HMD may be a display device worn on thehead of a user. For example, the HMD may be used to realize VR, AR orMR. For example, the drone may be a flying object that flies by wirelesscontrol signals without a person therein. For example, the VR device mayinclude a device that implements objects or backgrounds of a virtualworld. For example, the AR device may include a device that connects andimplements objects or background of a virtual world to objects,backgrounds, or the like of a real world. For example, the MR device mayinclude a device that unites and implements objects or background of avirtual world to objects, backgrounds, or the like of a real world. Forexample, the hologram device may include a device that implements360-degree 3D images by recording and playing 3D information using theinterference phenomenon of light that is generated by two lasers meetingeach other which is called holography. For example, the public safetydevice may include an image repeater or an imaging device that can beworn on the body of a user. For example, the MTC device and the IoTdevice may be devices that do not require direct interference oroperation by a person. For example, the MTC device and the IoT devicemay include a smart meter, a bending machine, a thermometer, a smartbulb, a door lock, various sensors, or the like. For example, themedical device may be a device that is used to diagnose, treat,attenuate, remove, or prevent diseases. For example, the medical devicemay be a device that is used to diagnose, treat, attenuate, or correctinjuries or disorders. For example, the medial device may be a devicethat is used to examine, replace, or change structures or functions. Forexample, the medical device may be a device that is used to controlpregnancy. For example, the medical device may include a device formedical treatment, a device for operations, a device for (external)diagnose, a hearing aid, an operation device, or the like. For example,the security device may be a device that is installed to prevent adanger that is likely to occur and to keep safety. For example, thesecurity device may be a camera, a CCTV, a recorder, a black box, or thelike. For example, the Fin Tech device may be a device that can providefinancial services such as mobile payment.

Referring to FIG. 1 , the first communication device 910 and the secondcommunication device 920 include processors 911 and 921, memories 914and 924, one or more Tx/Rx radio frequency (RF) modules 915 and 925, Txprocessors 912 and 922, Rx processors 913 and 923, and antennas 916 and926. The Tx/Rx module is also referred to as a transceiver. Each Tx/Rxmodule 915 transmits a signal through each antenna 926. The processorimplements the aforementioned functions, processes and/or methods. Theprocessor 921 may be related to the memory 924 that stores program codeand data. The memory may be referred to as a computer-readable medium.More specifically, the Tx processor 912 implements various signalprocessing functions with respect to L1 (i.e., physical layer) in DL(communication from the first communication device to the secondcommunication device). The Rx processor implements various signalprocessing functions of L1 (i.e., physical layer).

UL (communication from the second communication device to the firstcommunication device) is processed in the first communication device 910in a way similar to that described in association with a receiverfunction in the second communication device 920. Each Tx/Rx module 925receives a signal through each antenna 926. Each Tx/Rx module providesRF carriers and information to the Rx processor 923. The processor 921may be related to the memory 924 that stores program code and data. Thememory may be referred to as a computer-readable medium.

B. Signal Transmission/Reception Method in Wireless Communication System

FIG. 2 is a diagram showing an example of a signaltransmission/reception method in a wireless communication system.

Referring to FIG. 2 , when a UE is powered on or enters a new cell, theUE performs an initial cell search operation such as synchronizationwith a BS (S201). For this operation, the UE can receive a primarysynchronization channel (P-SCH) and a secondary synchronization channel(S-SCH) from the BS to synchronize with the BS and acquire informationsuch as a cell ID. In LTE and NR systems, the P-SCH and S-SCH arerespectively called a primary synchronization signal (PSS) and asecondary synchronization signal (SSS). After initial cell search, theUE can acquire broadcast information in the cell by receiving a physicalbroadcast channel (PBCH) from the BS. Further, the UE can receive adownlink reference signal (DL RS) in the initial cell search step tocheck a downlink channel state. After initial cell search, the UE canacquire more detailed system information by receiving a physicaldownlink shared channel (PDSCH) according to a physical downlink controlchannel (PDCCH) and information included in the PDCCH (S202).

Meanwhile, when the UE initially accesses the BS or has no radioresource for signal transmission, the UE can perform a random accessprocedure (RACH) for the BS (steps S203 to S206). To this end, the UEcan transmit a specific sequence as a preamble through a physical randomaccess channel (PRACH) (S203 and S205) and receive a random accessresponse (RAR) message for the preamble through a PDCCH and acorresponding PDSCH (S204 and S206). In the case of a contention-basedRACH, a contention resolution procedure may be additionally performed.

After the UE performs the above-described process, the UE can performPDCCH/PDSCH reception (S207) and physical uplink shared channel(PUSCH)/physical uplink control channel (PUCCH) transmission (S208) asnormal uplink/downlink signal transmission processes. Particularly, theUE receives downlink control information (DCI) through the PDCCH. The UEmonitors a set of PDCCH candidates in monitoring occasions set for oneor more control element sets (CORESET) on a serving cell according tocorresponding search space configurations. A set of PDCCH candidates tobe monitored by the UE is defined in terms of search space sets, and asearch space set may be a common search space set or a UE-specificsearch space set. CORESET includes a set of (physical) resource blockshaving a duration of one to three OFDM symbols. A network can configurethe UE such that the UE has a plurality of CORESETs. The UE monitorsPDCCH candidates in one or more search space sets. Here, monitoringmeans attempting decoding of PDCCH candidate(s) in a search space. Whenthe UE has successfully decoded one of PDCCH candidates in a searchspace, the UE determines that a PDCCH has been detected from the PDCCHcandidate and performs PDSCH reception or PUSCH transmission on thebasis of DCI in the detected PDCCH. The PDCCH can be used to schedule DLtransmissions over a PDSCH and UL transmissions over a PUSCH. Here, theDCI in the PDCCH includes downlink assignment (i.e., downlink grant (DLgrant)) related to a physical downlink shared channel and including atleast a modulation and coding format and resource allocationinformation, or an uplink grant (UL grant) related to a physical uplinkshared channel and including a modulation and coding format and resourceallocation information.

An initial access (IA) procedure in a 5G communication system will beadditionally described with reference to FIG. 2 .

The UE can perform cell search, system information acquisition, beamalignment for initial access, and DL measurement on the basis of an SSB.The SSB is interchangeably used with a synchronization signal/physicalbroadcast channel (SS/PBCH) block.

The SSB includes a PSS, an SSS and a PBCH. The SSB is configured in fourconsecutive OFDM symbols, and a PSS, a PBCH, an SSS/PBCH or a PBCH istransmitted for each OFDM symbol. Each of the PSS and the SSS includesone OFDM symbol and 127 subcarriers, and the PBCH includes 3 OFDMsymbols and 576 subcarriers.

Cell search refers to a process in which a UE acquires time/frequencysynchronization of a cell and detects a cell identifier (ID) (e.g.,physical layer cell ID (PCI)) of the cell. The PSS is used to detect acell ID in a cell ID group and the SSS is used to detect a cell IDgroup. The PBCH is used to detect an SSB (time) index and a half-frame.

There are 336 cell ID groups and there are 3 cell IDs per cell ID group.A total of 1008 cell IDs are present. Information on a cell ID group towhich a cell ID of a cell belongs is provided/acquired through an SSS ofthe cell, and information on the cell ID among 336 cell ID groups isprovided/acquired through a PSS.

The SSB is periodically transmitted in accordance with SSB periodicity.A default SSB periodicity assumed by a UE during initial cell search isdefined as 20 ms. After cell access, the SSB periodicity can be set toone of {5 ms, 10 ms, 20 ms, 40 ms, 80 ms, 160 ms} by a network (e.g., aBS).

Next, acquisition of system information (SI) will be described.

SI is divided into a master information block (MIB) and a plurality ofsystem information blocks (SIBs). SI other than the MIB may be referredto as remaining minimum system information. The MIB includesinformation/parameter for monitoring a PDCCH that schedules a PDSCHcarrying SIB1 (SystemInformationBlock1) and is transmitted by a BSthrough a PBCH of an SSB. SIB1 includes information related toavailability and scheduling (e.g., transmission periodicity andSI-window size) of the remaining SIBs (hereinafter, SIBx, x is aninteger equal to or greater than 2). SiBx is included in an SI messageand transmitted over a PDSCH. Each SI message is transmitted within aperiodically generated time window (i.e., SI-window).

A random access (RA) procedure in a 5G communication system will beadditionally described with reference to FIG. 2 .

A random access procedure is used for various purposes. For example, therandom access procedure can be used for network initial access,handover, and UE-triggered UL data transmission. A UE can acquire ULsynchronization and UL transmission resources through the random accessprocedure. The random access procedure is classified into acontention-based random access procedure and a contention-free randomaccess procedure. A detailed procedure for the contention-based randomaccess procedure is as follows.

A UE can transmit a random access preamble through a PRACH as Msg1 of arandom access procedure in UL. Random access preamble sequences havingdifferent two lengths are supported. A long sequence length 839 isapplied to subcarrier spacings of 1.25 kHz and 5 kHz and a shortsequence length 139 is applied to subcarrier spacings of 15 kHz, 30 kHz,60 kHz and 120 kHz.

When a BS receives the random access preamble from the UE, the BStransmits a random access response (RAR) message (Msg2) to the UE. APDCCH that schedules a PDSCH carrying a RAR is CRC masked by a randomaccess (RA) radio network temporary identifier (RNTI) (RA-RNTI) andtransmitted. Upon detection of the PDCCH masked by the RA-RNTI, the UEcan receive a RAR from the PDSCH scheduled by DCI carried by the PDCCH.The UE checks whether the RAR includes random access responseinformation with respect to the preamble transmitted by the UE, that is,Msg1. Presence or absence of random access information with respect toMsg1 transmitted by the UE can be determined according to presence orabsence of a random access preamble ID with respect to the preambletransmitted by the UE. If there is no response to Msg1, the UE canretransmit the RACH preamble less than a predetermined number of timeswhile performing power ramping. The UE calculates PRACH transmissionpower for preamble retransmission on the basis of most recent pathlossand a power ramping counter.

The UE can perform UL transmission through Msg3 of the random accessprocedure over a physical uplink shared channel on the basis of therandom access response information. Msg3 can include an RRC connectionrequest and a UE ID. The network can transmit Msg4 as a response toMsg3, and Msg4 can be handled as a contention resolution message on DL.The UE can enter an RRC connected state by receiving Msg4.

C. Beam Management (BM) Procedure of 5G Communication System

ABM procedure can be divided into (1) a DL MB procedure using an SSB ora CSI-RS and (2) a UL BM procedure using a sounding reference signal(SRS). In addition, each BM procedure can include Tx beam swiping fordetermining a Tx beam and Rx beam swiping for determining an Rx beam.

The DL BM procedure using an SSB will be described.

Configuration of a beam report using an SSB is performed when channelstate information (CSI)/beam is configured in RRC_CONNECTED.

-   -   A UE receives a CSI-ResourceConfig IE including        CSI-SSB-ResourceSetList for SSB resources used for BM from a BS.        The RRC parameter “csi-SSB-ResourceSetList” represents a list of        SSB resources used for beam management and report in one        resource set. Here, an SSB resource set can be set as {SSB×1,        SSB×2, SSB×3, SSB×4, . . . }. An SSB index can be defined in the        range of 0 to 63.    -   The UE receives the signals on SSB resources from the BS on the        basis of the CSI-SSB-ResourceSetList.    -   When CSI-RS reportConfig with respect to a report on SSBRI and        reference signal received power (RSRP) is set, the UE reports        the best SSBRI and RSRP corresponding thereto to the BS. For        example, when reportQuantity of the CSI-RS reportConfig IE is        set to ssb-Index-RSRP′, the UE reports the best SSBRI and RSRP        corresponding thereto to the BS.

When a CSI-RS resource is configured in the same OFDM symbols as an SSBand ‘QCL-TypeD’ is applicable, the UE can assume that the CSI-RS and theSSB are quasi co-located (QCL) from the viewpoint of ‘QCL-TypeD’. Here,QCL-TypeD may mean that antenna ports are quasi co-located from theviewpoint of a spatial Rx parameter. When the UE receives signals of aplurality of DL antenna ports in a QCL-TypeD relationship, the same Rxbeam can be applied.

Next, a DL BM procedure using a CSI-RS will be described.

An Rx beam determination (or refinement) procedure of a UE and a Tx beamswiping procedure of a BS using a CSI-RS will be sequentially described.A repetition parameter is set to ‘ON’ in the Rx beam determinationprocedure of a UE and set to ‘OFF’ in the Tx beam swiping procedure of aBS.

First, the Rx beam determination procedure of a UE will be described.

-   -   The UE receives an NZP CSI-RS resource set IE including an RRC        parameter with respect to ‘repetition’ from a BS through RRC        signaling. Here, the RRC parameter ‘repetition’ is set to ‘ON’.    -   The UE repeatedly receives signals on resources in a CSI-RS        resource set in which the RRC parameter ‘repetition’ is set to        ‘ON’ in different OFDM symbols through the same Tx beam (or DL        spatial domain transmission filters) of the BS.    -   The UE determines an RX beam thereof.    -   The UE skips a CSI report. That is, the UE can skip a CSI report        when the RRC parameter ‘repetition’ is set to ‘ON’.

Next, the Tx beam determination procedure of a BS will be described.

-   -   A UE receives an NZP CSI-RS resource set IE including an RRC        parameter with respect to ‘repetition’ from the BS through RRC        signaling. Here, the RRC parameter ‘repetition’ is related to        the Tx beam swiping procedure of the BS when set to ‘OFF’.    -   The UE receives signals on resources in a CSI-RS resource set in        which the RRC parameter ‘repetition’ is set to ‘OFF’ in        different DL spatial domain transmission filters of the BS.    -   The UE selects (or determines) a best beam.    -   The UE reports an ID (e.g., CRI) of the selected beam and        related quality information (e.g., RSRP) to the BS. That is,        when a CSI-RS is transmitted for BM, the UE reports a CRI and        RSRP with respect thereto to the BS.

Next, the UL BM procedure using an SRS will be described.

-   -   A UE receives RRC signaling (e.g., SRS-Config IE) including a        (RRC parameter) purpose parameter set to ‘beam management” from        a BS. The SRS-Config IE is used to set SRS transmission. The        SRS-Config IE includes a list of SRS-Resources and a list of        SRS-ResourceSets. Each SRS resource set refers to a set of        SRS-resources.

The UE determines Tx beamforming for SRS resources to be transmitted onthe basis of SRS-SpatialRelation Info included in the SRS-Config IE.Here, SRS-SpatialRelation Info is set for each SRS resource andindicates whether the same beamforming as that used for an SSB, a CSI-RSor an SRS will be applied for each SRS resource.

-   -   When SRS-SpatialRelationInfo is set for SRS resources, the same        beamforming as that used for the SSB, CSI-RS or SRS is applied.        However, when SRS-SpatialRelationInfo is not set for SRS        resources, the UE arbitrarily determines Tx beamforming and        transmits an SRS through the determined Tx beamforming.

Next, a beam failure recovery (BFR) procedure will be described.

In a beamformed system, radio link failure (RLF) may frequently occurdue to rotation, movement or beamforming blockage of a UE. Accordingly,NR supports BFR in order to prevent frequent occurrence of RLF. BFR issimilar to a radio link failure recovery procedure and can be supportedwhen a UE knows new candidate beams. For beam failure detection, a BSconfigures beam failure detection reference signals for a UE, and the UEdeclares beam failure when the number of beam failure indications fromthe physical layer of the UE reaches a threshold set through RRCsignaling within a period set through RRC signaling of the BS. Afterbeam failure detection, the UE triggers beam failure recovery byinitiating a random access procedure in a PCell and performs beamfailure recovery by selecting a suitable beam. (When the BS providesdedicated random access resources for certain beams, these areprioritized by the UE). Completion of the aforementioned random accessprocedure is regarded as completion of beam failure recovery.

D. URLLC (Ultra-Reliable and Low Latency Communication)

URLLC transmission defined in NR can refer to (1) a relatively lowtraffic size, (2) a relatively low arrival rate, (3) extremely lowlatency requirements (e.g., 0.5 and 1 ms), (4) relatively shorttransmission duration (e.g., 2 OFDM symbols), (5) urgentservices/messages, etc. In the case of UL, transmission of traffic of aspecific type (e.g., URLLC) needs to be multiplexed with anothertransmission (e.g., eMBB) scheduled in advance in order to satisfy morestringent latency requirements. In this regard, a method of providinginformation indicating preemption of specific resources to a UEscheduled in advance and allowing a URLLC UE to use the resources for ULtransmission is provided.

NR supports dynamic resource sharing between eMBB and URLLC. eMBB andURLLC services can be scheduled on non-overlapping time/frequencyresources, and URLLC transmission can occur in resources scheduled forongoing eMBB traffic. An eMBB UE may not ascertain whether PDSCHtransmission of the corresponding UE has been partially punctured andthe UE may not decode a PDSCH due to corrupted coded bits. In view ofthis, NR provides a preemption indication. The preemption indication mayalso be referred to as an interrupted transmission indication.

With regard to the preemption indication, a UE receivesDownlinkPreemption IE through RRC signaling from a BS. When the UE isprovided with DownlinkPreemption IE, the UE is configured with INT-RNTIprovided by a parameter int-RNTI in DownlinkPreemption IE for monitoringof a PDCCH that conveys DCI format 2_1. The UE is additionallyconfigured with a corresponding set of positions for fields in DCIformat 2_1 according to a set of serving cells and positionInDCI byINT-ConfigurationPerServing Cell including a set of serving cell indexesprovided by servingCellID, configured having an information payload sizefor DCI format 2_1 according to dci-Payloadsize, and configured withindication granularity of time-frequency resources according totimeFrequency Sect.

The UE receives DCI format 2_1 from the BS on the basis of theDownlinkPreemption IE.

When the UE detects DCI format 2_1 for a serving cell in a configuredset of serving cells, the UE can assume that there is no transmission tothe UE in PRBs and symbols indicated by the DCI format 2_1 in a set ofPRBs and a set of symbols in a last monitoring period before amonitoring period to which the DCI format 2_1 belongs. For example, theUE assumes that a signal in a time-frequency resource indicatedaccording to preemption is not DL transmission scheduled therefor anddecodes data on the basis of signals received in the remaining resourceregion.

E. mMTC (Massive MTC)

mMTC (massive Machine Type Communication) is one of 5G scenarios forsupporting a hyper-connection service providing simultaneouscommunication with a large number of UEs. In this environment, a UEintermittently performs communication with a very low speed andmobility. Accordingly, a main goal of mMTC is operating a UE for a longtime at a low cost. With respect to mMTC, 3GPP deals with MTC and NB(NarrowBand)-IoT.

mMTC has features such as repetitive transmission of a PDCCH, a PUCCH, aPDSCH (physical downlink shared channel), a PUSCH, etc., frequencyhopping, retuning, and a guard period.

That is, a PUSCH (or a PUCCH (particularly, a long PUCCH) or a PRACH)including specific information and a PDSCH (or a PDCCH) including aresponse to the specific information are repeatedly transmitted.Repetitive transmission is performed through frequency hopping, and forrepetitive transmission, (RF) retuning from a first frequency resourceto a second frequency resource is performed in a guard period and thespecific information and the response to the specific information can betransmitted/received through a narrowband (e.g., 6 resource blocks (RBs)or 1 RB).

F. Basic Operation Between Autonomous Vehicles Using 5G Communication

FIG. 3 shows an example of basic operations of an autonomous vehicle anda 5G network in a 5G communication system.

The autonomous vehicle transmits specific information to the 5G network(S1). The specific information may include autonomous driving relatedinformation. In addition, the 5G network can determine whether toremotely control the vehicle (S2). Here, the 5G network may include aserver or a module which performs remote control related to autonomousdriving. In addition, the 5G network can transmit information (orsignal) related to remote control to the autonomous vehicle (S3).

G. Applied Operations Between Autonomous Vehicle and 5G Network in 5GCommunication System

Hereinafter, the operation of an autonomous vehicle using 5Gcommunication will be described in more detail with reference towireless communication technology (BM procedure, URLLC, mMTC, etc.)described in FIGS. 1 and 2 .

First, a basic procedure of an applied operation to which a methodproposed by the present invention which will be described later and eMBBof 5G communication are applied will be described.

As in steps S1 and S3 of FIG. 3 , the autonomous vehicle performs aninitial access procedure and a random access procedure with the 5Gnetwork prior to step S1 of FIG. 3 in order to transmit/receive signals,information and the like to/from the 5G network.

More specifically, the autonomous vehicle performs an initial accessprocedure with the 5G network on the basis of an SSB in order to acquireDL synchronization and system information. A beam management (BM)procedure and a beam failure recovery procedure may be added in theinitial access procedure, and quasi-co-location (QCL) relation may beadded in a process in which the autonomous vehicle receives a signalfrom the 5G network.

In addition, the autonomous vehicle performs a random access procedurewith the 5G network for UL synchronization acquisition and/or ULtransmission. The 5G network can transmit, to the autonomous vehicle, aUL grant for scheduling transmission of specific information.Accordingly, the autonomous vehicle transmits the specific informationto the 5G network on the basis of the UL grant. In addition, the 5Gnetwork transmits, to the autonomous vehicle, a DL grant for schedulingtransmission of 5G processing results with respect to the specificinformation. Accordingly, the 5G network can transmit, to the autonomousvehicle, information (or a signal) related to remote control on thebasis of the DL grant.

Next, a basic procedure of an applied operation to which a methodproposed by the present invention which will be described later andURLLC of 5G communication are applied will be described.

As described above, an autonomous vehicle can receive DownlinkPreemptionIE from the 5G network after the autonomous vehicle performs an initialaccess procedure and/or a random access procedure with the 5G network.Then, the autonomous vehicle receives DCI format 2_1 including apreemption indication from the 5G network on the basis ofDownlinkPreemption IE. The autonomous vehicle does not perform (orexpect or assume) reception of eMBB data in resources (PRBs and/or OFDMsymbols) indicated by the preemption indication. Thereafter, when theautonomous vehicle needs to transmit specific information, theautonomous vehicle can receive a UL grant from the 5G network.

Next, a basic procedure of an applied operation to which a methodproposed by the present invention which will be described later and mMTCof 5G communication are applied will be described.

Description will focus on parts in the steps of FIG. 3 which are changedaccording to application of mMTC.

In step S1 of FIG. 3 , the autonomous vehicle receives a UL grant fromthe 5G network in order to transmit specific information to the 5Gnetwork. Here, the UL grant may include information on the number ofrepetitions of transmission of the specific information and the specificinformation may be repeatedly transmitted on the basis of theinformation on the number of repetitions. That is, the autonomousvehicle transmits the specific information to the 5G network on thebasis of the UL grant. Repetitive transmission of the specificinformation may be performed through frequency hopping, the firsttransmission of the specific information may be performed in a firstfrequency resource, and the second transmission of the specificinformation may be performed in a second frequency resource. Thespecific information can be transmitted through a narrowband of 6resource blocks (RBs) or 1 RB.

The above-described 5G communication technology can be combined withmethods proposed in the present invention which will be described laterand applied or can complement the methods proposed in the presentinvention to make technical features of the methods concrete and clear.

FIG. 4 schematically illustrates a system in which an indoor air qualitycontrol method using an intelligent air cleaner according to anembodiment of the present invention is implemented.

Referring to FIG. 4 , a system in which the indoor air quality controlmethod using an intelligent air cleaner according to an embodiment ofthe present invention is implemented may include an intelligent aircleaner 10 and a cloud server 20.

The intelligent air cleaner 10 can transmit dust concentration datasensed in the intelligent air cleaner 10 to the cloud server 20 byperforming data communication with the cloud server 20.

The cloud server 20 can perform AI processing on the basis of varioustypes of indoor dust concentration data collected from the intelligentair cleaner 10. The cloud server 20 includes an AI system, an AI moduleand an AI apparatus for performing AI processing and each of the AIsystem, the AI module and the AI apparatus may include at least onelearning model. The cloud server 20 can transmit AI processing resultswith respect to dust concentration data received from the intelligentair cleaner 10 to the intelligent air cleaner 10 or transmit a controlsignal of the intelligent air cleaner 10 according to AI processingresults.

The Korea Meteorological Administration (KMA) server 30 may transmitlocation-based air pollution information to the cloud server 20. Thelocation-based air pollution information may provide an observationpoint, an observation time, and a forecast (good, normal, bad, or verybad air quality). According to an embodiment of the present invention,when the KMA server provides location-based real-time observed airquality information to the cloud server 20, the cloud server 20 mayprovide a service of continuously driving the intelligent air cleaner10, ending the operation of the air cleaner, recommending ventilationsimultaneously with the operation of the air cleaner, or the like on thebasis of dust concentration data collected through an indoor air cleanerand external air pollution level information of the point where the aircleaner is located.

Meanwhile, the cloud server 20 transmits the aforementioned ventilationrecommendation service to a user terminal 40 such that the user canactively select whether or not to ventilate.

FIG. 5 is a block diagram of an AI apparatus applicable to embodimentsof the present invention.

Referring to FIG. 5 , an AI apparatus 20 may include an electronicdevice including an AI module which can perform AI processing, a serverincluding the AI module, or the like. Further, the AI apparatus 20 maybe included as at least a component of an air cleaner to perform atleast part of AI processing.

AI processing can include all operations related to a controller 140 ofan air cleaner. For example, the air cleaner can execute AI processingof air cleanliness or humidity information to performprocessing/determination, control signal generation operations.

The AI apparatus 20 may be a client device that directly uses AIprocessing results or a device in a cloud environment which provides AIprocessing results to other devices. The AI apparatus 20 is a computingdevice capable of learning a neural network and may be implemented invarious electronic devices such as a server, a desktop PC, a notebookPC, and a tablet PC.

The AI apparatus 20 may include an AI processor 21, a memory 25 and/or acommunication unit 27.

The AI processor 21 may learn a neural network using a program stored inthe memory 25. In particular, the AI processor 21 may learn a neuralnetwork for recognizing data related to the air cleaner. Here, theneural network for recognizing data related to the air cleaner may bedesigned to simulate the structure of the human brain on a computer andmay include a plurality of network nodes having weights that simulateneurons of the human neural network. The plurality of network nodes maytransmit and receive data according to a connection relationship tosimulate neuron synaptic activity of transmitting and receiving signalsthrough synapses. Here, the neural network may include a deep learningmodel developed from a neural network model. In the deep learning model,a plurality of network nodes may exchange data according to aconvolution connection relationship while being located in differentlayers. Examples of neural network models include various deep learningtechniques such as deep neural networks (DNNs), convolutional deepneural networks (CNNs), recurrent Boltzmann machines (RNNs), restrictedBoltzmann machines (RBMs), deep belief networks (DBN), and deepQ-network, and may be applied to fields such as computer vision, speechrecognition, natural language processing, and audio/signal processing.

Meanwhile, the processor performing the functions described above may bea general-purpose processor (e.g., CPU), or may be an AI-only processor(e.g., GPU) for artificial intelligence learning.

The memory 25 may store various programs and data necessary for theoperation of the AI apparatus 20. The memory 25 may be implemented as anon-volatile memory, a volatile memory, a flash-memory, a hard diskdrive (HDD), or a solid state drive (SDD). The memory 25 is accessed bythe AI processor 21, and reading/writing/correction/deletion/updating ofdata by the AI processor 21 can be performed. In addition, the memory 25may store a neural network model (e.g., a deep learning model 26)generated through a learning algorithm for dataclassification/recognition according to an embodiment of the presentinvention.

The AI processor 21 may include a data learning unit 22 that learns aneural network for data classification/recognition. The data learningunit 22 may learn criteria regarding which training data will be used todetermine data classification/recognition and how to classify andrecognize data using training data. The data learning unit 22 mayacquire training data to be used for learning and learn the deeplearning model by applying the acquired training data to the deeplearning model.

The data learning unit 22 may be manufactured in the form of at leastone hardware chip and mounted on the AI apparatus 20. For example, thedata learning unit 22 may be manufactured in the form of a dedicatedhardware chip for artificial intelligence (AI), or manufactured as apart of a general-purpose processor (CPU) or a graphics-only processor(GPU) and mounted on the AI apparatus 20. Further, the data learningunit 22 may be implemented as a software module. When the data learningunit 22 is implemented as a software module (or a program moduleincluding instructions), the software module may be stored innon-transitory computer readable media. In this case, at least onesoftware module may be provided by an operating system (OS) or anapplication.

The data learning unit 22 may include a training data acquisition unit23 and a model training unit 24.

The training data acquisition unit 23 may acquire training data requiredfor a neural network model for classifying and recognizing data.

The model training unit 24 may train the neural network model such thatit has a criterion for determining how to classify predetermined datausing the acquired training data. In this case, the model training unit24 may train the neural network model through supervised learning usingat least some of training data as a criterion. Alternatively, the modeltraining unit 24 may train the neural network model through unsupervisedlearning in which a criterion is discovered by self-learning usingtraining data without supervision. In addition, the model training unit24 may train the neural network model through reinforcement learningusing feedback regarding whether results of determination of a situationaccording to learning are correct. Further, the model training unit 24may train the neural network model using a learning algorithm includingerror back-propagation or gradient decent.

When the neural network model is trained, the model training unit 24 maystore the trained neural network model in the memory. The model trainingunit 24 may store the trained neural network model in a memory of aserver connected to the AI apparatus 20 through a wired or wirelessnetwork.

The data learning unit 22 may further include a training datapreprocessing unit (not shown) and a training data selection unit (notshown) to improve analysis results of a recognition model or saveresources or time necessary to generate the recognition model.

The training data preprocessing unit may preprocess acquired data suchthat the acquired data can be used for learning for situationdetermination. For example, the training data preprocessing unit mayprocess acquired data into a preset format such that the model trainingunit 24 can use acquired training data for learning for imagerecognition.

In addition, the training data selection unit may select data necessaryfor learning from among training data acquired by the training dataacquisition unit 23 and training data preprocessed by the preprocessingunit. The selected training data may be provided to the model trainingunit 24. For example, the training data selection unit,

In addition, the data learning unit 22 may further include a modelevaluation unit (not shown) to improve analysis results of the neuralnetwork model.

The model evaluation unit may input evaluation data to the neuralnetwork model, and when analysis results output from the evaluation datado not satisfy a predetermined criterion, cause the model training unit22 to re-perform training. In this case, the evaluation data may bepredefined data for evaluating the recognition model. For example, themodel evaluation unit may evaluate that the predetermined criterion isnot satisfied when the number of pieces of or ratio of evaluation datafor which analysis results are not accurate among analysis results ofthe trained recognition model for the evaluation data exceeds a presetthreshold.

The communication unit 27 may transmit AI processing results obtained bythe AI processor 21 to an external electronic device. For example,external electronic devices may include Bluetooth devices, self-drivingvehicles, robots, drones, AR devices, mobile devices, home appliances,and the like.

Although the AI apparatus 20 shown in FIG. 5 is functionally dividedinto the AI processor 21, the memory 25, the communication unit 27, andthe like, the above-described components may be integrated into onemodule and referred to as an AI module.

FIG. 6 is an exemplary block diagram of an indoor air quality controldevice using the intelligent air cleaner according to an embodiment ofthe present invention.

Referring to FIG. 6 , the intelligent air cleaner 10 may transmit datarequiring AI processing to the AI apparatus 20 through a communicationunit, and the AI apparatus 20 including the deep learning model 26 maytransmit AI processing results obtained using the deep learning model 26to the intelligent air cleaner 10. Refer to details described withreference to FIG. 5 for the AI apparatus 20.

Referring to FIG. 6 , the intelligent air cleaner 10 according to anembodiment of the present invention may include a sensor unit 115including one or more sensors for sensing various types of data, thecontroller 140 for controlling overall operations, and a driving unit180 that controls operations of an indoor fan, a heat exchanger, avalve, a wind direction adjusting unit, and the like provided inside themain body according to control of the control unit 140.

In addition, the intelligent air cleaner 10 according to an embodimentof the present invention may include a purification unit (not shown)including one or more filters, and the sensor unit 115 may include anair quality sensor that measures indoor air quality during operation.

In this case, the controller 140 may perform control to calculate afilter pollution level based on data measured by the air quality sensorand the driving time during the operation, add the calculated filterpollution level to a pre-stored filter pollution level, and outputfilter replacement notification information when the filter pollutionlevel satisfies a filter replacement criterion. It is more preferablethat the purification unit include a filter unit in which a plurality offilters is stacked. In this case, it is preferable that the filterreplacement criterion be set for each filter.

In addition, the intelligent air cleaner 10 according to an embodimentof the present invention may further include one or more of an audioinput unit 120 that receives user voice commands, a memory 150 thatstores various types of data, a communication unit 170 that wirelesslycommunicates with other electronic devices, a display 192 that displayspredetermined information as an image, and an audio output unit 191 thatoutputs predetermined information as audio.

The audio input unit 120 may receive an external audio signal and a uservoice command. To this end, the audio input unit 120 may include one ormore microphones MIC. Further, in order to more accurately receive auser voice command, the audio input unit 120 may include a plurality ofmicrophones 121 and 122. The plurality of microphones 121 and 122 may bespaced apart from each other and disposed at different locations, andmay obtain an external audio signal and process it into an electricalsignal.

The microphones 121 and 122 may be attached to an inner surface of acenter panel assembly 10B and may be placed in contact with or adjacentto a microphone hole.

Although FIG. 5 illustrates an example in which the audio input unit 120includes two microphones, the first microphone 121 and the secondmicrophone 122, the present invention is not limited thereto.

The audio input unit 120 may include a processing unit that convertsanalog sound into digital data or may be connected to the processingunit such that a user input voice command can be converted into datathat can be recognized by the controller 140 or a predetermined server.

Meanwhile, the audio input unit 120 may use various noise cancellationalgorithms to remove noise generated in the process of receiving a uservoice command.

In addition, the audio input unit 120 may include components for audiosignal processing, such as a filter that removes noise from audiosignals received through the microphones 121 and 122 and an amplifierthat amplifies and outputs a signal output from the filter.

The memory 150 records various types of information necessary for theoperation of the intelligent air cleaner 10 and may include volatile ornon-volatile recording media. Recording media store data that can beread by a microprocessor and may include a hard disk drive (HDD), asolid state disk (SSD), a silicon disk drive (SDD), a ROM, a RAM, aCD-ROM, magnetic tapes, floppy disks, optical data storage devices, andthe like.

Control data used for operating the air cleaner may be stored in thememory 150.

In addition, the memory 150 may store operation time of the air cleaner,and data necessary for calculation and determination of data, an airpollution level, a filter pollution level, and the like sensed by thesensor unit 115.

Depending on an embodiment, a sound source file of a voice command inputby a user may be stored in the memory 150, and the stored sound sourcefile may be transmitted to a voice recognition server system through thecommunication unit 170. In addition, the stored sound source file may bedeleted after the elapse of a preset time or after a preset operation isperformed.

Meanwhile, data for voice recognition may be stored in the memory 150,and the controller 140 may process a user voice input signal receivedthrough the audio input unit 120 and perform a voice recognitionprocess.

Alternatively, the intelligent air cleaner 10 may include a voicerecognition module (not shown), and the voice recognition module mayperform simple voice recognition such as call word recognition accordingto an embodiment.

Further, a call word determination algorithm for determining whether avoice signal includes a call word may be stored in the memory 150.

The controller 140 and the voice recognition module may determinewhether the voice signal includes the call word based on the call worddetermination algorithm.

Meanwhile, simple voice recognition may be performed by the intelligentair cleaner 10, and high-level voice recognition such as naturallanguage processing may be performed by a voice recognition serversystem.

For example, when a wake-up voice signal including a preset call word isreceived, the intelligent air cleaner 10 may switch to a state forreceiving a voice command. In this case, the intelligent air cleaner 10may perform only the voice recognition process until operation ofchecking whether voice including a call word is input, and subsequentvoice recognition for user voice input may be performed through thevoice recognition server system.

Since the system resources of the intelligent air cleaner 10 arelimited, complex natural language recognition and processing may beperformed through the voice recognition server system.

Alternatively, determination of whether or not voice including a callword is input may be double-performed by the intelligent air cleaner 10and the voice recognition server system. Accordingly, misrecognition ofcall word voice input determination can be reduced and a recognitionrate can be improved.

Limited data may be stored in the memory 150. For example, data forrecognizing a wake-up voice signal including a preset call word may bestored in the memory 150. In this case, the controller 140 may recognizea wake-up voice signal including a preset call word from a user voiceinput signal received through the audio input unit 120.

Meanwhile, a call word may be set by the manufacturer. For example, “LGWhissen” may be set as a call word. In addition, settings of s call wordmay be changed by a user.

The controller 140 may control a user voice command input afterrecognizing the wake-up voice signal to be transmitted to the voicerecognition server system through the communication unit 170.

The communication unit 170 includes one or more communication modulesand may perform wireless communication with other electronic devicesaccording to a predetermined communication method to transmit andreceive various signals.

Here, the predetermined communication method may be a Wi-Ficommunication method. Accordingly, a communication module included inthe intelligent air cleaner 10 may be a Wi-Fi communication module, butthe present invention is not limited to the communication method.

Alternatively, the intelligent air cleaner 10 may include a differenttype of communication module or a plurality of communication modules.For example, the intelligent air cleaner 10 may include an NFC module, aZigbee communication module, a Bluetooth communication module, and thelike.

The intelligent air cleaner 10 can be connected to a server included ina voice recognition server system, a predetermined external server, auser's portable terminal, and the like through a Wi-Fi communicationmodule, etc., and can support smart functions such as remote monitoringand remote control.

The user may check information about the intelligent air cleaner 10 orcontrol the intelligent air cleaner 10 through the portable terminal.

In addition, the communication unit 170 may communicate with an accesspoint (AP) device and communicate with other devices by accessing awireless Internet network through the access point device.

In addition, the controller 140 may transmit state information of theintelligent air cleaner 10, a user voice command, and the like to avoice recognition server system through the communication unit 170.

Meanwhile, when a control signal is received through the communicationunit 170, the controller 140 may control the intelligent air cleaner 10such that the intelligent air cleaner 10 operates according to thereceived control signal.

The display 192 may display information corresponding to user commandinput, a processing result corresponding to the user command input, anoperation mode, an operation state, an error state, filter replacementinformation, and the like as images.

According to an embodiment, the display 192 may be configured as a touchscreen by forming a mutual layer structure with a touch pad. In thiscase, the display 192 may also be used as an input device capable ofinputting information by a user touch in addition to an output device.

In addition, the audio output unit 191 may output notification messagessuch as warning sound, an operation mode, an operation state, and anerror state, information corresponding to user command input, and aprocessing result corresponding to the user command input as audio underthe control of the controller 140. Meanwhile, the audio output unit 191may convert an electrical signal from the controller 140 into an audiosignal and output the converted audio signal. To this end, the audiooutput unit 191 may include a speaker or the like.

The controller 140 may control the audio output unit 191 and the display192 to provide predetermined information to the user throughvisual/auditory means in each step of a voice recognition process and aprocess of controlling the intelligent air cleaner 10.

The driving unit 180 controls the amount of air discharged to an indoorspace by controlling rotation of a motor connected to the indoor fan. Inaddition, the driving unit 180 controls the operation of the heatexchanger such that the heat exchanger exchanges heat with thesurrounding air by evaporating or condensing a refrigerant suppliedthereto.

The driving unit 180 is a device for controlling the direction of airdischarged to the indoor space in response to a control command of thecontroller 140, and changes the direction of the discharged air upward,downward, to the left and to the right when an outlet is opened. Thedriving unit 180 may include a vane driving unit for driving a vane, afan driving unit for driving a fan, and the like under the control ofthe controller 140.

Meanwhile, the driving unit 180 may include a motor driving unit, andmay include an inverter or the like to drive the motor.

The intelligent air cleaner 10 may further include an operation unit 130for user input and a camera 110 capable of imaging a predetermined rangearound the intelligent air cleaner 10.

The operation unit 130 may include a plurality of operation buttons andtransmit an input signal corresponding to a button to the controller140.

The camera 110 captures images of the surroundings of the intelligentair cleaner 10, the external environment, and the like, and a pluralityof cameras may be installed for imaging efficiency.

For example, the camera 110 may include an image sensor (e.g., CMOSimage sensor) including at least one optical lens and a plurality ofphotodiodes (e.g., pixels) forming images by light passing through theoptical lens, and a digital signal processor (DSP) that composes animage based on signals output from the photodiodes. The digital signalprocessor can create not only still images but also moving imagescomposed of frames of still images.

According to an embodiment, it is possible to determine the presence orabsence of an occupant and position information based on images obtainedthrough the camera 110.

Meanwhile, an image captured by the camera 110 may be stored in thememory 150.

The intelligent air cleaner 10 according to an embodiment of the presentinvention may include the sensor unit 115 having one or more sensors.

For example, the sensor unit 115 may include one or more temperaturesensors for sensing indoor and outdoor temperatures, a humidity sensorfor sensing humidity, an air quality sensor for sensing air quality suchas an amount of dust, and the like. Further, the sensor unit 115 mayfurther include a human body detection sensor for sensing the presenceor absence of an occupant and/or location of the occupant according toan embodiment.

The sensor unit 115 may sense temperature and humidity data of an indoorenvironment in which the intelligent air cleaner 10 is installed. Inaddition, the sensor unit 115 may sense air quality, such as the amountof carbon dioxide and the amount of fine dust, in an indoor environmentin which the intelligent air cleaner 10 is installed.

The sensor unit 115 may continuously collect data regarding temperature,humidity, and air quality. Alternatively, the sensor unit 115 maycollect data regarding temperature, humidity, and air quality atpredetermined time intervals.

In addition, the controller 140 may control the operation of theintelligent air cleaner 10 based on data sensed by the sensor unit 115.

The controller 140 may perform control to calculate a filter pollutionlevel based on data measured by the air quality sensor of the sensorunit 115 and operation time during operation, add the calculated filterpollution level to a pre-stored filter pollution level, and outputfilter replacement notification information when the filter pollutionlevel satisfies a filter replacement criterion. Simply counting anoperation time to determine whether to replace the filter does notreflect the degree of pollution that varies depending on the useenvironment. Therefore, the filter replacement cycle can be determinedmore accurately by reflecting both the data measured by the air qualitysensor and the operation time to reflect a weighted value according tothe air pollution level during driving to the operation time.

In addition, the controller 140 may determine the amount of inflow airbased on the air volume during the operation time and the operationtime, determine an air pollution level based on data measured by the airquality sensor during the operation, and calculate the filter pollutionlevel based on the amount of inflow air and the air pollution level.

A filter replacement time may be predicted based on a usage time, airvolume, and air condition information using an air quality sensorprovided to provide indoor air quality information to the user withoutadding a dedicated device for checking a filter pollution level.

On the other hand, in the prior art, when the pollution level of indoorair exceeds a predetermined reference value regardless of theenvironment in which an air cleaner is used, the air cleaner is operatedregardless of the pollution level of outdoor air, thereby reducing thelifespan of the filter. However, according to one embodiment of thepresent invention, when the indoor air quality is lower than the outdoorair quality, the lifespan of the filter can be efficiently managed byinducing ventilation rather than operation of the air cleaner.

Meanwhile, the audio output unit 191 may output a voice guidance messagefor guiding ventilation recommendation information by voice in responseto ventilation recommendation of the cloud server 20 under the controlof the controller 140. By notifying of ventilation recommendationinformation through the voice guidance message, it is possible toefficiently manage the air cleaner by reflecting real-time external airpollution.

Meanwhile, the display 192 may display the ventilation recommendationinformation.

It is possible to determine the presence or absence of a user in apredetermined space and position information of the user based on dataobtained by the camera 110 or the sensor unit 115 according to anembodiment. In addition, the controller 140 may determine whether theuser is approaching through the camera 110 or the sensor unit 115. Inthis case, the controller 140 may control the audio output unit 191and/or the display 192 to output ventilation recommendation informationwhen a user's approach is detected.

According to an embodiment, the controller 140 may control ventilationrecommendation information to be transmitted to other electronic devicesthrough the communication unit 170. For example, the intelligent aircleaner 10 may transmit the ventilation recommendation information to apredetermined server, a user's portable terminal, and the like, therebypreventing the user from forcefully continuously driving the intelligentair cleaner 10 with another device and allowing the air cleaner to beefficiently managed through ventilation.

FIG. 7 illustrates a data flow for implementing an indoor air qualitycontrol method using an intelligent air cleaner according to anembodiment of the present invention.

Referring to FIG. 7 , the indoor air quality control method using anintelligent air cleaner according to an embodiment of the presentinvention can be implemented through data communication with theintelligent air cleaner 10, a 5G network and a Korea MeteorologicalAdministration (KMA) server. Here, the 5G network may include the cloudserver 20 in the present invention. The 5G network is called the cloudserver 20 for convenience of description. Further, the air cleaner 10may refer to the intelligent air cleaner 10 in this specification.

Referring to FIG. 7 , the cloud server 20 can transmit a control signalfor setting the intelligent air cleaner 10 to an artificial intelligencemode to the air cleaner 10 (S700).

When the air cleaner 10 operates in a normal mode distinguished from theartificial intelligence mode, a ventilation recommendation serviceprovided in an embodiment of the present invention may not be provided.However, only an alarm is provided while the ventilation recommendationservice is provided even in the normal mode, and the ventilationrecommendation service can be output through an output unit when thealarm is provided and operation of the air cleaner 10 can becontinuously performed in the normal mode. However, when the ventilationrecommendation service is provided in the artificial intelligence mode,operation of the air cleaner is automatically terminated and anoperation of controlling a window to be opened through a window systemconnected to a home network service can be performed.

When the artificial intelligence mode is set, the intelligent aircleaner 10 can sense dust concentration data through a sensing unit(S710).

In the artificial intelligence mode, the intelligent air cleaner 10 candetermine whether dust concentration data sensed for a predeterminedtime exceeds a reference value (S720). For example, the intelligence aircleaner 10 can store the average of PM 2.5 data for five minutes. Whensix or more pieces of data are generated on the basis of low airquality, dust data can be transmitted to the cloud server 20 (S730).

The air cleaner 10 can transmit dust concentration data to the cloudserver 20 whenever a PM 2.5 fine dust concentration is generated sixtimes on the basis of low air quality according to the aforementionedstandard. Here, PM-2.5 (Particulate Matter Less than 2.5 μm) refers todust with a particle size of 2.5 μm or less. This is referred to as afine particulate matter. According to results that a smaller particulatesize greatly affects health, advanced countries have startedintroduction of criteria for particulate matters from the late 90s.Korea announced criteria of an annual average of 25 μg/m³ and an averageof 35 μg/m³ for twenty-four hours, and the US set criteria of an annualaverage of 15 μg/m³ and an average of 35 μg/m³ for twenty-four hours.This is called fine particulate matters.

Meanwhile, the cloud server 20 can determine whether ventilation isrequired with reference to a degree of outside air pollution of a pointwhere the air cleaner 10 is positioned on the basis of dustconcentration data collected from the air cleaner 10. To this end, thecloud server 20 can receive outside dust concentrations from the KMAserver (30 in FIG. 4 ) (S740).

The cloud server 20 can compare a degree of indoor air pollutioncollected from the air cleaner 10 with KMA server data to determinenecessity for ventilation (S750).

Further, the cloud server 20 can transmit a ventilation recommendationalarm message to the air cleaner 10. The air cleaner 10 can output thereceived ventilation recommendation alarm message to a display unit orthrough an audio output unit. Further, the cloud server 20 may transmitthe recommendation alarm message to a user terminal.

FIG. 8 is a flowchart of the indoor air quality control method using anintelligent air cleaner according to an embodiment of the presentinvention. FIG. 8 illustrates an operation in the cloud server 20 andthe operation can be implemented through a processor of the cloud server20.

Referring to FIG. 8 , the processor of the cloud server 20 can receiveindoor dust concentration data collected by the air cleaner (S800).

Here, the received dust concentration data may be data that exceeds apredetermined reference value among data sensed at predeterminedintervals. As described above, the air cleaner 10 stores sensed dustconcentration data and can store an average of PM 2.5 data. Further, theair cleaner 10 can transmit indoor dust concentration data to the cloudserver 20 when six or more reference values for high indoor fine dustconcentrations are generated on the basis of PM 2.5 data.

The processor can predict indoor dust concentration progress by applyingdust concentration data received from the air cleaner to a learningmodel (S810).

The processor can apply the received dust concentration data as inputvalues of a deep learning model stored in a memory of the cloud server20. Indoor air quality progress after a time when dust concentrationdata received from the air cleaner is collected can be predicted throughoutput values of the deep learning model. Here, “indoor dustconcentration progress” can refer to indoor air quality changes overtime. More specifically, it can refer to a tendency with respect towhether indoor air quality decreases or increases.

The processor can predict the tendency by applying the dustconcentration data collected from the air cleaner to an artificialintelligence learning model. The artificial intelligence learning modelused to predict the tendency is not limited to the aforementioned deeplearning model and the above-described deep learning algorithms such asMLP, CNN and RNN can be applied in various manners.

The processor can receive outside dust concentration data from the KMAserver (S820).

The processor can determine whether ventilation is required on the basisof a result value of prediction of indoor dust concentration progressand outside dust concentration data (S830).

For example, the processor can determine that ventilation is notrequired when it is determined that the predicted indoor dustconcentration progress is a pattern of gradual improvement. Further,when the predicted indoor dust concentration progress is determined tobe a tendency to degrade, the processor can compare the indoor dustconcentration progress with KMA server data. When an indoor dustconcentration is higher than an outside dust concentration, furtheroperation of the air cleaner is ended and it can be determined thatventilation is required. However, the indoor dust concentration is lowerthan the outside dust concentration, it can be determined thatventilation is not required.

The processor can transmit a ventilation alarm message to the aircleaner or the user terminal when there is a necessity for ventilationaccording to a result of determination of whether ventilation isrequired (S840).

Hereinafter, an operation of determining necessity for ventilation inthe cloud server 20 will be described in more detail with reference toFIG. 9 .

FIG. 9 is a diagram for describing a detailed operation of the cloudserver for performing the indoor air quality control method using anintelligent air cleaner according to an embodiment of the presentinvention. In FIG. 9 , operation of a 5G network is described asoperation of the cloud server, more specifically, the processor of thecloud server.

Referring to FIG. 9 , the processor can perform a process of preparingdata in order to use indoor dust concentration data received from theair cleaner 10 for AI processing (S900).

As described above, when dust concentrations measured by a sensor of theair cleaner 10 are equal to or greater than a predetermined referencevalue N time or more continuously, the cloud server 20 receives thedata. However, the present invention is not limited thereto andintervals at which dust concentration data is received from the aircleaner may be varied in various manners. Accordingly, it is possible todetermine whether dust data concentrations exceed the reference value Ntimes continuously through AI processing (S910).

The processor can apply N pieces of dust data as input values of a DNNmodel (S920) and predict indoor dust concentration progress throughoutputs of the DNN model (S930).

If dust concentrations sensed N times continuously for a predeterminedtime are less than the reference value, the processor can continuouslyprepare indoor dust concentration data received from the air cleanerwithout performing AI processing (S910: N).

When a predicted pattern is determined to be a dust concentrationincreasing pattern (S940): Y), the processor can control interoperationwith the KMA system (S950). For example, the processor can requestoutside dust concentration data from the KMA server. The processor canreceive an outside dust concentration from the KMA server in response tothe request (S960).

The processor determines that ventilation is required (S980) when anaverage value of DNN input data is greater than an outside dustconcentration (S970).

Hereinafter, standards for learning a deep learning model having dustconcentration data received from the air cleaner 10 as input values willbe described.

FIG. 10 is a diagram for describing deep learning model learningstandards according to an embodiment of the present invention.

Referring to FIG. 10 , the air cleaner 10 can collect a large amount ofindoor dust concentration data for twenty-four hours, for example. Whenpattern analysis is required for certain dust concentration data, apattern in which collected dust concentrations exceed a predeterminedreference value needs to be analyzed.

In the graph shown in FIG. 10 , section A (1 hour) shows an example inwhich measured dust concentrations are higher than a reference value inthe entire section for 1 hour, and section B (1 hour) shows a pattern inwhich measured data concentrations are higher than the reference valueonly for first 30 minutes and lower than the reference value for last 30minutes. According to a conventional statistical approach for dustconcentration data, dust concentration data needs to be collected forone hour and observed in order to predict dust concentration progress ineach of section A and section B.

However, when embodiments of the present invention are applied, the deeplearning model is learned such that a tendency of dust concentrationdata for last 30 minutes is predicted on the basis of a pattern of dustconcentration data for first 30 minutes in section A. That is, the deeplearning model can be learned by setting data for first 30 minutes ofsection A to input values of the deep learning model and supervisingdata for last 30 minutes as output values of the deep learning model.Likewise, the deep learning model can be learned by setting data forfirst 30 minutes of section B to input values of the deep learning modeland supervising data for last 30 minutes as output values of the deeplearning model.

According to the learned deep learning model, it is possible to predictthat indoor dust concentration progress measured for last 30 minuteswill be continuously higher than the reference value according to dustconcentration data collected for first 30 minutes in the case of sectionA. Likewise, according to the learned deep learning model, it ispossible to predict that indoor dust concentration progress measured forlast 30 minutes will be continuously lower than the reference valueaccording to dust concentration data collected for first 30 minutes inthe case of section B.

Accordingly, when the indoor air quality control method using anintelligent air cleaner according to an embodiment of the presentinvention is applied, 50% or more of dust concentration data which is anobject of collection and analysis for ventilation recommendation can bereduced. Furthermore, it is possible to secure accuracy equal to orhigher than that of dust concentration progress prediction according toa conventional statistical approach while reducing the amount of datarequired.

FIG. 11 is a diagram for describing various examples of indoor airquality patterns used for deep learning model learning through dataclustering according to an embodiment of the present invention. FIG. 12is a diagram for describing a deep learning model learning patternsthrough input factors and result factors according to an embodiment ofthe present invention.

Referring to FIG. 11 , there may be an air quality pattern (ventilationnon-recommendation) in which fine dust concentrations remain in a highstate for a certain time and then become lower. Further, there may bepatterns (recommendation of ventilation) in which fine dustconcentrations are irregular for time periods or continuously remain ina high state, or a high state and a low state are repeated for a shorttime. According to an embodiment of the present invention, an AIprocessor of the cloud server can classify air quality patterns shown inFIG. 11 into a ventilation non-recommendation pattern and a ventilationrecommendation pattern.

Referring to FIG. 12 , after the aforementioned classification standardis defined, a deep learning model can be learned such that an output ofthe deep learning model has a “ventilation recommendation” or“ventilation non-recommendation” value for each of a plurality of inputfactors.

FIG. 12 is a diagram referred to for description of deep learning.

Deep learning, a kind of machine learning, is multi-level learning froma deep level on the basis of data. Deep learning can indicate a set ofmachine learning algorithms that use multiple layers to extractessential data from a plurality of pieces of data.

A deep learning architecture can include an artificial neural network(ANN) and can be composed of, for example, deep neural network (DNN)such as a convolutional neural network (CNN), a recurrent neural network(RNN) or a deep belief network (DBN).

Referring to FIG. 12 , the ANN can include an input layer, a hiddenlayer and an output layer. Each layer includes a plurality of nodes andis connected to the next layer. Nodes between neighboring layers can beconnected to each other having weights.

A computer (machine) discovers a specific pattern from input dataapplied thereto to form a feature map. The computer (machine) canextract low-level features, intermediate-level features and high-levelfeatures to recognize an object and output a recognition result.

The ANN can abstract the next layer as features of a higher level.

Referring to FIG. 12 , each node can operate on the basis of anactivation model, and an output value corresponding to an input valuecan be determined according to the activation model.

An arbitrary node, for example, an output value of a low-level feature,can be input to the next layer connected to the corresponding node, forexample, a node of an intermediate-level feature. A node of the nextlayer, for example, a node of an intermediate-level feature, can receivevalues output from a plurality of nodes of low-level features.

Here, an input value of each node may be a value obtained by applying aweight to an output value of a node of a previous layer. A weight canrefer to strength of connection between nodes.

Further, deep learning may be regarded as a process of discovering anappropriate weight.

Further, an arbitrary node, for example, an output value of anintermediate-level feature, can be input to the next layer connected tothe corresponding node, for example, a node of a high-level feature. Anode of the next layer, for example, a node of a high-level feature, canreceive values output from a plurality of nodes of intermediate-levelfeatures.

An ANN can extract feature information corresponding to each level usinga learned layer corresponding to each level. The ANN can performsequential abstraction to recognize a predetermined object using featureinformation of the highest level.

For example, in a face recognition process using deep learning, acomputer can distinguish bright pixels and dark pixels from an inputimage according to pixel brightness, detect simple forms such asoutlines and edges and then detect more complicated forms and objects.Finally, the computer can detect a form that defines a human face.

A deep learning architecture according to the present invention can usevarious known architectures. For example, the deep learning architectureaccording to the present invention may be a convolutional neural network(CNN), a recurrent neural network (RNN), a deep belief network (DBN) orthe like.

The RNN is mainly used for natural language processing, is anarchitecture effective for processing time-series data varying overtime, and can accumulate layers every moment to constitute an artificialneural network architecture. The DBN is a deep learning architectureconfigured by accumulating a restricted Boltzman machine (RBM) that is adeep learning technique in multiple layers. When RBM learning isrepeated to achieve a predetermined number of layers, a DNB having thecorresponding number of layers can be formed. The CNN is a modelsimulating the human brain function, which is generated on theassumption that a person extracts basic features of an object and thenrecognizes the object on the basis of results of complicatedcalculations in the brain when the person recognizes the object.

Meanwhile, learning of an artificial neural network can be performed byadjusting a weight of a connection line between nodes such that adesired output is obtained for a given input (adjusting a bias ifrequired). Further, an artificial neural network can continuously updateweight values through learning. In addition, a method such as backpropagation may be used for learning of an artificial neural network.

Furthermore, the AI apparatus may store weights and biases constitutingthe DNN architecture according to an embodiment. Further, weights andbiases constituting the DNN may be stored in an embedded memory of apattern recognition module.

Referring to FIG. 12 , the AI processor in the cloud server can updateweight values applied to the deep learning model such that a specificoutput (ventilation recommendation or ventilation non-recommendation) isoutput on the basis of input factors shown in FIG. 12 .

Further, referring to FIG. 12 , when input factors (e.g., D1 to D6) areinput to a trained deep learning model, a ventilation recommendation orventilation non-recommendation result value may be derived.

As an example of an input factor, D1 may be an average value of dustconcentration data for 1 to 5 minutes. D2 may be an average value ofdust concentration data for 6 to 10 minutes on the basis of PM 2.5. D3to D6 can be defined in the same manner. Although the aforementionedinput factors have been described as examples, similar result values canbe output within a similar reliability range through dust concentrationpattern prediction of a learned deep learning model even though anyinput factor is applied to the learned deep learning model.

FIG. 13 is a diagram for describing an example of determining necessityfor ventilation using a learned deep learning model according to anembodiment of the present invention.

Referring to FIG. 13 , the AI processor of the cloud server 20 can setindoor dust concentration data received from the air cleaner 10 as inputvalues of a deep learning model. The input values may be PM 2.5 averagevalues every five minutes and may have different dust concentrationaverage values.

Referring to FIGS. 12 and 13 , the AI processor of the cloud server 20can assign a first weight Weight_1 to data input to an input layer, adda first bias bias_1 thereto and transfer the resultant value to a firsthidden layer (S1310).

The AI processor can assign a second weight Weight_2 to data input tothe first hidden layer, add a second bias bias_2 thereto and transferthe resultant value to a second hidden layer (S1320). Learningoperations (S1330, S1340 and S1350) in the same pattern can be performedon the remaining third and fourth hidden layers. A series of processesof outputting a value from an input layer to an output layer throughhidden layers in the deep learning model can be realized by a separateprocessor or a separate program which controls the deep learning model.

The number of hidden layers and the number of outputs of each hiddenlayer may depend on a deep learning network architecture. According toan embodiment of the present invention, if the first hidden layer isdefined as 20 unit, the second hidden layer is defined as 16 unit, thethird hidden layer is defined as 10 unit, and the fourth hidden layer isdefined as 4 unit, the last output layer is 1 unit and can be configuredto have an output value of “1” when ventilation is required and anoutput value of “0” when ventilation is not required.

FIGS. 14 and 15 are diagrams for describing results of tests to whichthe indoor air quality control method using an intelligent air cleaneraccording to an embodiment of the present invention is applied.

FIG. 14 illustrates results of a first test of recommending ventilationfor 50 minutes on the basis of data collected by the air cleaner for 30minutes. As a result of the first test, when the test is performed aftera DNN model is trained such that ventilation recommendation is performed2979 times and ventilation non-recommendation is performed 2089 times,accuracy of 78.4% can be achieved. As a result of a second test ofrecommending ventilation for 60 minutes on the basis of data collectedfor 30 minutes, a DNN model with accuracy of 71.3% can be implemented.

The above-described present invention can be implemented withcomputer-readable code in a computer-readable medium in which programhas been recorded. The computer-readable medium may include all kinds ofrecording devices capable of storing data readable by a computer system.Examples of the computer-readable medium may include a hard disk drive(HDD), a solid state disk (SSD), a silicon disk drive (SDD), a ROM, aRAM, a CD-ROM, magnetic tapes, floppy disks, optical data storagedevices, and the like and also include such a carrier-wave typeimplementation (for example, transmission over the Internet). Therefore,the above embodiments are to be construed in all aspects as illustrativeand not restrictive. The scope of the invention should be determined bythe appended claims and their legal equivalents, not by the abovedescription, and all changes coming within the meaning and equivalencyrange of the appended claims are intended to be embraced therein.

The invention claimed is:
 1. An indoor air quality control method usingan air cleaner configured to acquire indoor dust concentration data anda cloud server configured to receive the indoor dust concentration datafrom the air cleaner, comprising: receiving, from the air cleaner, dustconcentration data of an indoor place where the air cleaner is located;predicting indoor dust concentration progress on the basis of outputvalues of a learning model having the received indoor dust concentrationdata as input values; receiving outside dust concentration data from anexternal server; determining whether ventilation is required bycomparing the predicted indoor dust concentration progress with theoutside dust concentration data; and controlling an alarm to be outputto the air cleaner or a mobile terminal associated with the air cleaneraccording to whether ventilation is required, wherein the receivedindoor dust concentration data is data exceeding a predeterminedreference value among data sensed at predetermined intervals.
 2. Theindoor air quality control method of claim 1, wherein the receiving ofthe indoor dust concentration data from the air cleaner comprisesreceiving a certain percentage or higher of data in a bad state on thebasis of particulate matter less than 2.5 μm forecast from among thereceived indoor dust concentration data sensed by the air cleaner atpredetermined intervals.
 3. The indoor air quality control method ofclaim 1, wherein the predicting of the indoor dust concentrationprogress comprises: determining whether the received indoor dustconcentration data continuously sensed N-th times exceeds thepredetermined reference value; and defining data of N dustconcentrations as input values of a deep learning model and predictingthe indoor dust concentration progress through output values of the deeplearning model.
 4. The indoor air quality control method of claim 3,further comprising, when progress of additional N dust concentrationssensed after the N dust concentrations are sensed is predicted toincrease as a result of prediction of the indoor dust concentrationprogress: requesting the outside dust concentration data from ameteorological administration server; and determining that ventilationis required when an average value of input data of the deep learningmodel is greater than the outside dust concentration data received fromthe meteorological administration server.
 5. The indoor air qualitycontrol method of claim 1, wherein the indoor dust concentrationprogress is predicted as one of a pattern in which the same number ofdust concentrations as the number of pieces of the received indoor dustconcentration data received from the air cleaner increase, a pattern inwhich the dust concentrations decrease and a pattern in which the dustconcentrations remains in a current state.
 6. The indoor air qualitycontrol method of claim 5, wherein the determining of whetherventilation is required comprises determining that ventilation isrequired when the indoor dust concentration progress is determined to bethe increasing pattern or the current state remaining pattern.
 7. Theindoor air quality control method of claim 5, wherein the determining ofwhether ventilation is required comprises: determining that ventilationis not required when the indoor dust concentration progress is predictedas the increasing pattern and an outside dust concentration receivedfrom the external server is higher than the indoor dust concentration;and controlling the air cleaner to continuously operate.
 8. The indoorair quality control method of claim 5, wherein the determining ofwhether ventilation is required further comprises predicting aventilation time, wherein it is determined that ventilation is requiredwhen the indoor dust concentration progress is predicted as thedecreasing pattern and it is predicted that the outside dustconcentration is lower than the indoor dust concentration at a specifictime of the decreasing pattern.
 9. The indoor air quality control methodof claim 1, wherein the controlling of output of the alarm comprisescontrolling an indoor dust concentration state and whether ventilationis required to be output through audio.
 10. The indoor air qualitycontrol method of claim 1, wherein the receiving of the indoor dustconcentration data further comprises receiving, in a state in which theair cleaner is powered off, the indoor dust concentration data sensedthrough a sensor in a state in which the air cleaner is in a standbystate, further comprising activating the air cleaner when it isdetermined that ventilation is required.
 11. The indoor air qualitycontrol method of claim 1, wherein the determining of whetherventilation is required comprises adaptively controlling a ventilationrecommendation standard in consideration of characteristics of anoccupant residing in the indoor place.
 12. An indoor air quality controlapparatus using an air cleaner configured to acquire indoor dustconcentration data and a cloud server configured to receive the indoordust concentration data from the air cleaner, the cloud servercomprising: a radio frequency (RF) communication module; a memoryconfigured to store a deep learning model; and a processor configured todetermine whether to perform ventilation in a space in which the aircleaner is located on the basis of indoor dust concentration datareceived from the air cleaner and outside dust concentration datareceived from a meteorological administration server, wherein thereceived indoor dust concentration data is data exceeding apredetermined reference value among data sensed at predeterminedintervals, and wherein the processor is configured to predict indoordust concentration progress on the basis of output values of the deeplearning model having the received indoor dust concentration data asinput values and is configured to control an alarm to be output to theair cleaner or a mobile terminal associated with the air cleaneraccording to whether ventilation is required.
 13. The indoor air qualitycontrol apparatus of claim 12, wherein the processor is configured todetermine whether the received indoor dust concentration datacontinuously sensed N-th times exceeds the predetermined referencevalue, is configured to define data of N dust concentrations as inputvalues of the deep learning model and is configured to predict theindoor dust concentration progress through output values of the deeplearning model.
 14. The indoor air quality control apparatus of claim12, wherein, when progress of additional N dust concentrations sensedafter the N dust concentrations are sensed is predicted to increase as aresult of prediction of the indoor dust concentration progress, theprocessor is configured to request the outside dust concentration datafrom the meteorological administration server and is configured todetermine that ventilation is required when an average value of inputdata of the deep learning model is greater than the outside dustconcentration data received from the meteorological administrationserver.
 15. An indoor air quality control system, comprising: an aircleaner configured to acquire indoor dust concentration data; and acloud server configured to receive the indoor dust concentration datafrom the air cleaner, wherein the received dust concentration data isdata exceeding a predetermined reference value among data sensed by theair cleaner at predetermined intervals, and wherein the cloud server isconfigured to: predict indoor dust concentration progress on the basisof output values of a deep learning model having the received indoordust concentration data as input values, receive outside dustconcentration data from an external server, determine whetherventilation is required by comparing the predicted indoor dustconcentration progress with the outside dust concentration data, andoutput an alarm to the air cleaner or a mobile terminal associated withthe air cleaner according to whether ventilation is required.
 16. Anon-transitory computer-readable medium storing a computer-executablecomponent configured to be executed in one or more processors of acomputer device, the computer-executable component being configured: toreceive, from an air cleaner, dust concentration data of an indoor placewhere the air cleaner is located; to predict indoor dust concentrationprogress on the basis of output values of a learning model having thereceived indoor dust concentration data as input values; to receiveoutside dust concentration data from an external server; to determinewhether ventilation is required by comparing the predicted indoor dustconcentration progress with the outside dust concentration data; and tocontrol an alarm to be output to the air cleaner or a mobile terminalassociated with the air cleaner according to whether ventilation isrequired, wherein the received indoor dust concentration data includesdata exceeding a predetermined reference value among data sensed atpredetermined intervals.